Overview

Dataset statistics

Number of variables27
Number of observations5571
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory247.7 B

Variable types

Numeric8
Categorical19

Alerts

HCPCS_CODE has a high cardinality: 561 distinct valuesHigh cardinality
COUNTY has a high cardinality: 297 distinct valuesHigh cardinality
CODE has a high cardinality: 1023 distinct valuesHigh cardinality
PROCEDURE_CODE has a high cardinality: 135 distinct valuesHigh cardinality
PROCEDURE_DESCRIPTION has a high cardinality: 122 distinct valuesHigh cardinality
APPROVED_CHARGE is highly overall correlated with TOTAL_CHARGESHigh correlation
REASON_CODE is highly overall correlated with CLAIM_STATUS and 1 other fieldsHigh correlation
Age is highly overall correlated with MEDICARE_STATUSHigh correlation
TOTAL_CHARGES is highly overall correlated with APPROVED_CHARGEHigh correlation
ENCOUNTER_TYPE is highly overall correlated with PLACE_OF_SERVICE_CODEHigh correlation
CLAIM_STATUS is highly overall correlated with REASON_CODE and 1 other fieldsHigh correlation
DENIAL_CATEGORY is highly overall correlated with REASON_CODE and 1 other fieldsHigh correlation
PLACE_OF_SERVICE_CODE is highly overall correlated with ENCOUNTER_TYPE and 1 other fieldsHigh correlation
MEDICARE_STATUS is highly overall correlated with AgeHigh correlation
ADMIT_TYPE_CODE is highly overall correlated with PLACE_OF_SERVICE_CODEHigh correlation
BILL_TYPE_CODE is highly imbalanced (54.8%)Imbalance
RACE is highly imbalanced (68.3%)Imbalance
APPROVED_CHARGE has 56 (1.0%) zerosZeros
TOTAL_CHARGES has 56 (1.0%) zerosZeros

Reproduction

Analysis started2023-05-08 16:28:49.120913
Analysis finished2023-05-08 16:28:58.440178
Duration9.32 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

APPROVED_CHARGE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2867
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1736.9049
Minimum-82.7
Maximum28868.84
Zeros56
Zeros (%)1.0%
Negative28
Negative (%)0.5%
Memory size87.0 KiB
2023-05-08T21:58:58.509634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-82.7
5-th percentile13.86
Q153.17
median252.46
Q31764.945
95-th percentile8062.57
Maximum28868.84
Range28951.54
Interquartile range (IQR)1711.775

Descriptive statistics

Standard deviation3128.3475
Coefficient of variation (CV)1.8011047
Kurtosis12.147919
Mean1736.9049
Median Absolute Deviation (MAD)227.35
Skewness2.9423478
Sum9676297
Variance9786558.3
MonotonicityNot monotonic
2023-05-08T21:58:58.618860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
1.0%
7902.57 13
 
0.2%
6667.57 13
 
0.2%
8062.57 11
 
0.2%
3659.73 11
 
0.2%
1251.77 10
 
0.2%
4.75 10
 
0.2%
7827.07 10
 
0.2%
1878.39 10
 
0.2%
3649.63 10
 
0.2%
Other values (2857) 5417
97.2%
ValueCountFrequency (%)
-82.7 2
< 0.1%
-56.55 2
< 0.1%
-37.1 2
< 0.1%
-36.43 2
< 0.1%
-35.34 2
< 0.1%
-35.11 1
< 0.1%
-32.96 1
< 0.1%
-29.51 2
< 0.1%
-27.05 2
< 0.1%
-26.92 1
< 0.1%
ValueCountFrequency (%)
28868.84 1
 
< 0.1%
27601.81 4
0.1%
26996.61 2
< 0.1%
26486.41 2
< 0.1%
26052.21 1
 
< 0.1%
19788.96 3
0.1%
19564.46 2
< 0.1%
19474.36 2
< 0.1%
19398.86 2
< 0.1%
16367.48 2
< 0.1%

REASON_CODE
Real number (ℝ)

Distinct36
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.439957
Minimum1
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:58:58.724707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q322
95-th percentile115
Maximum118
Range117
Interquartile range (IQR)21

Descriptive statistics

Standard deviation36.800624
Coefficient of variation (CV)1.8004257
Kurtosis1.6558012
Mean20.439957
Median Absolute Deviation (MAD)0
Skewness1.8072404
Sum113871
Variance1354.2859
MonotonicityNot monotonic
2023-05-08T21:58:58.963539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 3682
66.1%
115 320
 
5.7%
29 314
 
5.6%
90 311
 
5.6%
32 34
 
0.6%
12 30
 
0.5%
5 30
 
0.5%
7 30
 
0.5%
6 30
 
0.5%
4 30
 
0.5%
Other values (26) 760
 
13.6%
ValueCountFrequency (%)
1 3682
66.1%
4 30
 
0.5%
5 30
 
0.5%
6 30
 
0.5%
7 30
 
0.5%
8 29
 
0.5%
9 30
 
0.5%
10 30
 
0.5%
11 30
 
0.5%
12 30
 
0.5%
ValueCountFrequency (%)
118 28
 
0.5%
117 29
 
0.5%
116 29
 
0.5%
115 320
5.7%
114 28
 
0.5%
112 29
 
0.5%
111 29
 
0.5%
90 311
5.6%
76 29
 
0.5%
74 29
 
0.5%

Age
Real number (ℝ)

Distinct60
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.418955
Minimum19
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:58:59.076334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile55
Q171
median76
Q384
95-th percentile94
Maximum104
Range85
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.533418
Coefficient of variation (CV)0.16400928
Kurtosis2.8298925
Mean76.418955
Median Absolute Deviation (MAD)7
Skewness-0.99749688
Sum425730
Variance157.08656
MonotonicityNot monotonic
2023-05-08T21:58:59.183156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 386
 
6.9%
72 369
 
6.6%
73 334
 
6.0%
79 283
 
5.1%
90 235
 
4.2%
77 217
 
3.9%
84 212
 
3.8%
75 205
 
3.7%
71 205
 
3.7%
69 204
 
3.7%
Other values (50) 2921
52.4%
ValueCountFrequency (%)
19 30
 
0.5%
38 80
1.4%
39 19
 
0.3%
42 35
0.6%
43 15
 
0.3%
44 9
 
0.2%
45 18
 
0.3%
47 2
 
< 0.1%
49 13
 
0.2%
50 4
 
0.1%
ValueCountFrequency (%)
104 38
 
0.7%
101 22
 
0.4%
100 8
 
0.1%
99 71
1.3%
98 30
 
0.5%
97 7
 
0.1%
96 33
 
0.6%
95 34
 
0.6%
94 114
2.0%
93 78
1.4%

SERVICE_UNIT_QUANTITY
Real number (ℝ)

Distinct98
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8427571
Minimum0
Maximum510
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:58:59.291666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile12
Maximum510
Range510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.989115
Coefficient of variation (CV)4.3341251
Kurtosis131.2897
Mean4.8427571
Median Absolute Deviation (MAD)0
Skewness9.6802388
Sum26979
Variance440.54295
MonotonicityNot monotonic
2023-05-08T21:58:59.417181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4399
79.0%
2 399
 
7.2%
3 142
 
2.5%
4 135
 
2.4%
5 51
 
0.9%
6 50
 
0.9%
20 29
 
0.5%
100 29
 
0.5%
10 28
 
0.5%
8 27
 
0.5%
Other values (88) 282
 
5.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4399
79.0%
2 399
 
7.2%
3 142
 
2.5%
4 135
 
2.4%
5 51
 
0.9%
6 50
 
0.9%
7 24
 
0.4%
8 27
 
0.5%
9 10
 
0.2%
ValueCountFrequency (%)
510 1
< 0.1%
340 1
< 0.1%
321 1
< 0.1%
300 1
< 0.1%
296 1
< 0.1%
280 1
< 0.1%
266 1
< 0.1%
244 1
< 0.1%
240 1
< 0.1%
205 1
< 0.1%

TOTAL_CHARGES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3155
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1783.3414
Minimum-36.43
Maximum28868.84
Zeros56
Zeros (%)1.0%
Negative8
Negative (%)0.1%
Memory size87.0 KiB
2023-05-08T21:58:59.526804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-36.43
5-th percentile27.84
Q1108.62
median343.33
Q31786.52
95-th percentile8157.07
Maximum28868.84
Range28905.27
Interquartile range (IQR)1677.9

Descriptive statistics

Standard deviation3116.1498
Coefficient of variation (CV)1.7473658
Kurtosis12.295841
Mean1783.3414
Median Absolute Deviation (MAD)296.8
Skewness2.9589874
Sum9934995
Variance9710389.7
MonotonicityNot monotonic
2023-05-08T21:58:59.629699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
1.0%
7902.57 13
 
0.2%
6667.57 13
 
0.2%
8062.57 11
 
0.2%
3659.73 11
 
0.2%
3649.63 10
 
0.2%
1878.39 10
 
0.2%
1192.27 9
 
0.2%
9422.86 9
 
0.2%
8217.17 9
 
0.2%
Other values (3145) 5420
97.3%
ValueCountFrequency (%)
-36.43 2
 
< 0.1%
-35.11 1
 
< 0.1%
-32.96 1
 
< 0.1%
-26.92 1
 
< 0.1%
-25.92 1
 
< 0.1%
-24.3 1
 
< 0.1%
-21.77 1
 
< 0.1%
0 56
1.0%
0.35 1
 
< 0.1%
2.47 1
 
< 0.1%
ValueCountFrequency (%)
28868.84 1
 
< 0.1%
27782.81 2
< 0.1%
27601.81 2
< 0.1%
26996.61 2
< 0.1%
26486.41 2
< 0.1%
26052.21 1
 
< 0.1%
19788.96 3
0.1%
19564.46 2
< 0.1%
19474.36 2
< 0.1%
19398.86 2
< 0.1%

BILL_TYPE_CODE
Categorical

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
131
3381 
111
735 
141
391 
13I
 
254
851
 
243
Other values (23)
567 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16713
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row131
2nd row131
3rd row131
4th row131
5th row131

Common Values

ValueCountFrequency (%)
131 3381
60.7%
111 735
 
13.2%
141 391
 
7.0%
13I 254
 
4.6%
851 243
 
4.4%
137 208
 
3.7%
117 45
 
0.8%
133 40
 
0.7%
132 40
 
0.7%
11I 34
 
0.6%
Other values (18) 200
 
3.6%

Length

2023-05-08T21:58:59.724648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
131 3381
60.7%
111 735
 
13.2%
141 391
 
7.0%
13i 254
 
4.6%
851 243
 
4.4%
137 208
 
3.7%
117 45
 
0.8%
133 40
 
0.7%
132 40
 
0.7%
11i 34
 
0.6%
Other values (18) 200
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1 10851
64.9%
3 4032
 
24.1%
4 413
 
2.5%
7 336
 
2.0%
8 326
 
2.0%
I 319
 
1.9%
5 301
 
1.8%
2 89
 
0.5%
0 26
 
0.2%
9 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16394
98.1%
Uppercase Letter 319
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10851
66.2%
3 4032
 
24.6%
4 413
 
2.5%
7 336
 
2.0%
8 326
 
2.0%
5 301
 
1.8%
2 89
 
0.5%
0 26
 
0.2%
9 20
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
I 319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16394
98.1%
Latin 319
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10851
66.2%
3 4032
 
24.6%
4 413
 
2.5%
7 336
 
2.0%
8 326
 
2.0%
5 301
 
1.8%
2 89
 
0.5%
0 26
 
0.2%
9 20
 
0.1%
Latin
ValueCountFrequency (%)
I 319
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10851
64.9%
3 4032
 
24.1%
4 413
 
2.5%
7 336
 
2.0%
8 326
 
2.0%
I 319
 
1.9%
5 301
 
1.8%
2 89
 
0.5%
0 26
 
0.2%
9 20
 
0.1%

HCPCS_CODE
Categorical

Distinct561
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
36415
 
324
G0463
 
228
80053
 
176
85025
 
173
Q0510
 
149
Other values (556)
4521 

Length

Max length6
Median length5
Mean length5.0001795
Min length1

Characters and Unicode

Total characters27856
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)3.1%

Sample

1st row82306
2nd rowJ1644
3rd row76642
4th row85610
5th rowG0463

Common Values

ValueCountFrequency (%)
36415 324
 
5.8%
G0463 228
 
4.1%
80053 176
 
3.2%
85025 173
 
3.1%
Q0510 149
 
2.7%
80061 132
 
2.4%
80048 118
 
2.1%
84443 110
 
2.0%
A9270 109
 
2.0%
97110 91
 
1.6%
Other values (551) 3961
71.1%

Length

2023-05-08T21:58:59.817447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36415 324
 
5.8%
g0463 228
 
4.1%
80053 176
 
3.2%
85025 173
 
3.1%
q0510 149
 
2.7%
80061 132
 
2.4%
80048 118
 
2.1%
84443 110
 
2.0%
a9270 109
 
2.0%
97110 91
 
1.6%
Other values (551) 3961
71.1%

Most occurring characters

ValueCountFrequency (%)
0 4912
17.6%
8 3275
11.8%
3 2551
9.2%
9 2404
8.6%
5 2356
8.5%
1 2344
8.4%
4 2287
8.2%
7 2151
7.7%
6 2118
7.6%
2 1959
 
7.0%
Other values (12) 1499
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26357
94.6%
Uppercase Letter 1499
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 481
32.1%
A 300
20.0%
J 278
18.5%
Q 245
16.3%
C 79
 
5.3%
P 53
 
3.5%
Z 38
 
2.5%
S 10
 
0.7%
L 9
 
0.6%
V 3
 
0.2%
Other values (2) 3
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 4912
18.6%
8 3275
12.4%
3 2551
9.7%
9 2404
9.1%
5 2356
8.9%
1 2344
8.9%
4 2287
8.7%
7 2151
8.2%
6 2118
8.0%
2 1959
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 26357
94.6%
Latin 1499
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 481
32.1%
A 300
20.0%
J 278
18.5%
Q 245
16.3%
C 79
 
5.3%
P 53
 
3.5%
Z 38
 
2.5%
S 10
 
0.7%
L 9
 
0.6%
V 3
 
0.2%
Other values (2) 3
 
0.2%
Common
ValueCountFrequency (%)
0 4912
18.6%
8 3275
12.4%
3 2551
9.7%
9 2404
9.1%
5 2356
8.9%
1 2344
8.9%
4 2287
8.7%
7 2151
8.2%
6 2118
8.0%
2 1959
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4912
17.6%
8 3275
11.8%
3 2551
9.2%
9 2404
8.6%
5 2356
8.5%
1 2344
8.4%
4 2287
8.2%
7 2151
7.7%
6 2118
7.6%
2 1959
 
7.0%
Other values (12) 1499
 
5.4%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
female
3289 
male
2282 

Length

Max length6
Median length6
Mean length5.1807575
Min length4

Characters and Unicode

Total characters28862
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 3289
59.0%
male 2282
41.0%

Length

2023-05-08T21:58:59.905864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:00.002321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
female 3289
59.0%
male 2282
41.0%

Most occurring characters

ValueCountFrequency (%)
e 8860
30.7%
m 5571
19.3%
a 5571
19.3%
l 5571
19.3%
f 3289
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28862
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8860
30.7%
m 5571
19.3%
a 5571
19.3%
l 5571
19.3%
f 3289
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 28862
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8860
30.7%
m 5571
19.3%
a 5571
19.3%
l 5571
19.3%
f 3289
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8860
30.7%
m 5571
19.3%
a 5571
19.3%
l 5571
19.3%
f 3289
 
11.4%

RACE
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
white
4710 
black
519 
hispanic
 
141
other
 
76
asian
 
63
Other values (2)
 
62

Length

Max length21
Median length5
Mean length5.1057261
Min length5

Characters and Unicode

Total characters28444
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 4710
84.5%
black 519
 
9.3%
hispanic 141
 
2.5%
other 76
 
1.4%
asian 63
 
1.1%
unknown 59
 
1.1%
north american native 3
 
0.1%

Length

2023-05-08T21:59:00.077350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:00.179147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
white 4710
84.5%
black 519
 
9.3%
hispanic 141
 
2.5%
other 76
 
1.4%
asian 63
 
1.1%
unknown 59
 
1.1%
north 3
 
0.1%
american 3
 
0.1%
native 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 5061
17.8%
h 4930
17.3%
t 4792
16.8%
e 4792
16.8%
w 4769
16.8%
a 795
 
2.8%
c 663
 
2.3%
k 578
 
2.0%
l 519
 
1.8%
b 519
 
1.8%
Other values (9) 1026
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28438
> 99.9%
Space Separator 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5061
17.8%
h 4930
17.3%
t 4792
16.9%
e 4792
16.9%
w 4769
16.8%
a 795
 
2.8%
c 663
 
2.3%
k 578
 
2.0%
l 519
 
1.8%
b 519
 
1.8%
Other values (8) 1020
 
3.6%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28438
> 99.9%
Common 6
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5061
17.8%
h 4930
17.3%
t 4792
16.9%
e 4792
16.9%
w 4769
16.8%
a 795
 
2.8%
c 663
 
2.3%
k 578
 
2.0%
l 519
 
1.8%
b 519
 
1.8%
Other values (8) 1020
 
3.6%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5061
17.8%
h 4930
17.3%
t 4792
16.8%
e 4792
16.8%
w 4769
16.8%
a 795
 
2.8%
c 663
 
2.3%
k 578
 
2.0%
l 519
 
1.8%
b 519
 
1.8%
Other values (9) 1026
 
3.6%

STATE
Categorical

Distinct48
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
California
412 
Texas
410 
Florida
406 
Michigan
 
332
Indiana
 
278
Other values (43)
3733 

Length

Max length14
Median length12
Mean length8.3819781
Min length2

Characters and Unicode

Total characters46696
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowUtah
3rd rowDelaware
4th rowConnecticut
5th rowIowa

Common Values

ValueCountFrequency (%)
California 412
 
7.4%
Texas 410
 
7.4%
Florida 406
 
7.3%
Michigan 332
 
6.0%
Indiana 278
 
5.0%
Ohio 235
 
4.2%
Pennsylvania 230
 
4.1%
North Carolina 211
 
3.8%
New York 198
 
3.6%
New Jersey 181
 
3.2%
Other values (38) 2678
48.1%

Length

2023-05-08T21:59:00.276015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 425
 
6.7%
california 412
 
6.5%
texas 410
 
6.4%
florida 406
 
6.4%
michigan 332
 
5.2%
carolina 312
 
4.9%
indiana 278
 
4.4%
ohio 235
 
3.7%
pennsylvania 230
 
3.6%
north 211
 
3.3%
Other values (41) 3118
49.0%

Most occurring characters

ValueCountFrequency (%)
a 6050
13.0%
i 5421
 
11.6%
n 4441
 
9.5%
s 3453
 
7.4%
o 3346
 
7.2%
e 2901
 
6.2%
r 2409
 
5.2%
l 2044
 
4.4%
h 1457
 
3.1%
t 1263
 
2.7%
Other values (36) 13911
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39508
84.6%
Uppercase Letter 6390
 
13.7%
Space Separator 798
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6050
15.3%
i 5421
13.7%
n 4441
11.2%
s 3453
8.7%
o 3346
8.5%
e 2901
7.3%
r 2409
 
6.1%
l 2044
 
5.2%
h 1457
 
3.7%
t 1263
 
3.2%
Other values (14) 6723
17.0%
Uppercase Letter
ValueCountFrequency (%)
M 927
14.5%
C 845
13.2%
N 757
11.8%
I 618
9.7%
T 547
8.6%
O 447
7.0%
F 406
 
6.4%
W 318
 
5.0%
P 230
 
3.6%
Y 198
 
3.1%
Other values (11) 1097
17.2%
Space Separator
ValueCountFrequency (%)
798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45898
98.3%
Common 798
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6050
13.2%
i 5421
11.8%
n 4441
 
9.7%
s 3453
 
7.5%
o 3346
 
7.3%
e 2901
 
6.3%
r 2409
 
5.2%
l 2044
 
4.5%
h 1457
 
3.2%
t 1263
 
2.8%
Other values (35) 13113
28.6%
Common
ValueCountFrequency (%)
798
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6050
13.0%
i 5421
 
11.6%
n 4441
 
9.5%
s 3453
 
7.4%
o 3346
 
7.2%
e 2901
 
6.2%
r 2409
 
5.2%
l 2044
 
4.4%
h 1457
 
3.1%
t 1263
 
2.7%
Other values (36) 13911
29.8%

COUNTY
Categorical

Distinct297
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
Suffolk
 
107
Bailey
 
106
Cumberland
 
103
Palm Beach
 
98
La Porte
 
93
Other values (292)
5064 

Length

Max length19
Median length15
Mean length7.4799856
Min length3

Characters and Unicode

Total characters41671
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.2%

Sample

1st rowCherokee
2nd rowSummit
3rd rowNew Castle
4th rowFairfield
5th rowPottawattamie

Common Values

ValueCountFrequency (%)
Suffolk 107
 
1.9%
Bailey 106
 
1.9%
Cumberland 103
 
1.8%
Palm Beach 98
 
1.8%
La Porte 93
 
1.7%
Middlesex 86
 
1.5%
Fairfield 81
 
1.5%
King 80
 
1.4%
San Diego 79
 
1.4%
Warrick 73
 
1.3%
Other values (287) 4665
83.7%

Length

2023-05-08T21:59:00.377729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 154
 
2.4%
suffolk 107
 
1.7%
bailey 106
 
1.7%
cumberland 103
 
1.6%
la 102
 
1.6%
beach 101
 
1.6%
palm 98
 
1.5%
lake 96
 
1.5%
porte 93
 
1.4%
middlesex 86
 
1.3%
Other values (308) 5368
83.7%

Most occurring characters

ValueCountFrequency (%)
a 4364
 
10.5%
e 4093
 
9.8%
o 3134
 
7.5%
n 2746
 
6.6%
r 2612
 
6.3%
l 2458
 
5.9%
i 2338
 
5.6%
t 2013
 
4.8%
s 1697
 
4.1%
u 1102
 
2.6%
Other values (41) 15114
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34420
82.6%
Uppercase Letter 6398
 
15.4%
Space Separator 843
 
2.0%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4364
12.7%
e 4093
11.9%
o 3134
 
9.1%
n 2746
 
8.0%
r 2612
 
7.6%
l 2458
 
7.1%
i 2338
 
6.8%
t 2013
 
5.8%
s 1697
 
4.9%
u 1102
 
3.2%
Other values (15) 7863
22.8%
Uppercase Letter
ValueCountFrequency (%)
C 867
13.6%
S 774
12.1%
B 584
9.1%
M 553
 
8.6%
P 549
 
8.6%
W 434
 
6.8%
L 422
 
6.6%
D 375
 
5.9%
K 214
 
3.3%
H 210
 
3.3%
Other values (14) 1416
22.1%
Space Separator
ValueCountFrequency (%)
843
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40818
98.0%
Common 853
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4364
 
10.7%
e 4093
 
10.0%
o 3134
 
7.7%
n 2746
 
6.7%
r 2612
 
6.4%
l 2458
 
6.0%
i 2338
 
5.7%
t 2013
 
4.9%
s 1697
 
4.2%
u 1102
 
2.7%
Other values (39) 14261
34.9%
Common
ValueCountFrequency (%)
843
98.8%
- 10
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4364
 
10.5%
e 4093
 
9.8%
o 3134
 
7.5%
n 2746
 
6.6%
r 2612
 
6.3%
l 2458
 
5.9%
i 2338
 
5.6%
t 2013
 
4.8%
s 1697
 
4.1%
u 1102
 
2.6%
Other values (41) 15114
36.3%

CODE
Categorical

Distinct1023
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
I10
 
336
E785
 
185
I2510
 
157
E039
 
86
R079
 
84
Other values (1018)
4723 

Length

Max length7
Median length6
Mean length4.4763956
Min length3

Characters and Unicode

Total characters24938
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique405 ?
Unique (%)7.3%

Sample

1st rowG9340
2nd rowE1142
3rd rowI480
4th rowI10
5th rowI255

Common Values

ValueCountFrequency (%)
I10 336
 
6.0%
E785 185
 
3.3%
I2510 157
 
2.8%
E039 86
 
1.5%
R079 84
 
1.5%
K219 80
 
1.4%
Z79899 70
 
1.3%
E119 68
 
1.2%
Z7982 60
 
1.1%
Z1231 60
 
1.1%
Other values (1013) 4385
78.7%

Length

2023-05-08T21:59:00.477205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i10 336
 
6.0%
e785 185
 
3.3%
i2510 157
 
2.8%
e039 86
 
1.5%
r079 84
 
1.5%
k219 80
 
1.4%
z79899 70
 
1.3%
e119 68
 
1.2%
z1231 60
 
1.1%
z7982 60
 
1.1%
Other values (1013) 4385
78.7%

Most occurring characters

ValueCountFrequency (%)
1 3202
12.8%
0 2980
11.9%
9 2716
10.9%
8 1964
 
7.9%
2 1835
 
7.4%
5 1659
 
6.7%
7 1425
 
5.7%
4 1260
 
5.1%
3 1216
 
4.9%
I 1077
 
4.3%
Other values (22) 5604
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19192
77.0%
Uppercase Letter 5746
 
23.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1077
18.7%
R 978
17.0%
Z 903
15.7%
E 693
12.1%
M 440
7.7%
K 332
 
5.8%
N 250
 
4.4%
J 203
 
3.5%
D 140
 
2.4%
G 138
 
2.4%
Other values (12) 592
10.3%
Decimal Number
ValueCountFrequency (%)
1 3202
16.7%
0 2980
15.5%
9 2716
14.2%
8 1964
10.2%
2 1835
9.6%
5 1659
8.6%
7 1425
7.4%
4 1260
 
6.6%
3 1216
 
6.3%
6 935
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19192
77.0%
Latin 5746
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1077
18.7%
R 978
17.0%
Z 903
15.7%
E 693
12.1%
M 440
7.7%
K 332
 
5.8%
N 250
 
4.4%
J 203
 
3.5%
D 140
 
2.4%
G 138
 
2.4%
Other values (12) 592
10.3%
Common
ValueCountFrequency (%)
1 3202
16.7%
0 2980
15.5%
9 2716
14.2%
8 1964
10.2%
2 1835
9.6%
5 1659
8.6%
7 1425
7.4%
4 1260
 
6.6%
3 1216
 
6.3%
6 935
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3202
12.8%
0 2980
11.9%
9 2716
10.9%
8 1964
 
7.9%
2 1835
 
7.4%
5 1659
 
6.7%
7 1425
 
5.7%
4 1260
 
5.1%
3 1216
 
4.9%
I 1077
 
4.3%
Other values (22) 5604
22.5%

PRESENT_ON_ADMIT
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
0
2341 
N
1765 
Y
1465 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5571
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2341
42.0%
N 1765
31.7%
Y 1465
26.3%

Length

2023-05-08T21:59:00.566816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:00.652593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2341
42.0%
n 1765
31.7%
y 1465
26.3%

Most occurring characters

ValueCountFrequency (%)
0 2341
42.0%
N 1765
31.7%
Y 1465
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3230
58.0%
Decimal Number 2341
42.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1765
54.6%
Y 1465
45.4%
Decimal Number
ValueCountFrequency (%)
0 2341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3230
58.0%
Common 2341
42.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1765
54.6%
Y 1465
45.4%
Common
ValueCountFrequency (%)
0 2341
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2341
42.0%
N 1765
31.7%
Y 1465
26.3%

DUAL_STATUS
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
5
773 
3
722 
1
635 
8
630 
0
607 
Other values (5)
2204 

Length

Max length3
Median length1
Mean length1.0107701
Min length1

Characters and Unicode

Total characters5631
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row4
4th row2
5th row7

Common Values

ValueCountFrequency (%)
5 773
13.9%
3 722
13.0%
1 635
11.4%
8 630
11.3%
0 607
10.9%
6 594
10.7%
7 583
10.5%
2 546
9.8%
4 451
8.1%
Yes 30
 
0.5%

Length

2023-05-08T21:59:00.732213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:00.860666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
5 773
13.9%
3 722
13.0%
1 635
11.4%
8 630
11.3%
0 607
10.9%
6 594
10.7%
7 583
10.5%
2 546
9.8%
4 451
8.1%
yes 30
 
0.5%

Most occurring characters

ValueCountFrequency (%)
5 773
13.7%
3 722
12.8%
1 635
11.3%
8 630
11.2%
0 607
10.8%
6 594
10.5%
7 583
10.4%
2 546
9.7%
4 451
8.0%
Y 30
 
0.5%
Other values (2) 60
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5541
98.4%
Lowercase Letter 60
 
1.1%
Uppercase Letter 30
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 773
14.0%
3 722
13.0%
1 635
11.5%
8 630
11.4%
0 607
11.0%
6 594
10.7%
7 583
10.5%
2 546
9.9%
4 451
8.1%
Lowercase Letter
ValueCountFrequency (%)
e 30
50.0%
s 30
50.0%
Uppercase Letter
ValueCountFrequency (%)
Y 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5541
98.4%
Latin 90
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 773
14.0%
3 722
13.0%
1 635
11.5%
8 630
11.4%
0 607
11.0%
6 594
10.7%
7 583
10.5%
2 546
9.9%
4 451
8.1%
Latin
ValueCountFrequency (%)
Y 30
33.3%
e 30
33.3%
s 30
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 773
13.7%
3 722
12.8%
1 635
11.3%
8 630
11.2%
0 607
10.8%
6 594
10.5%
7 583
10.4%
2 546
9.7%
4 451
8.0%
Y 30
 
0.5%
Other values (2) 60
 
1.1%

ENCOUNTER_TYPE
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
Outpatient
1452 
Other
1178 
Emergency Department
1166 
Skilled Nursing Facility
901 
Acute Inpatient
874 

Length

Max length24
Median length15
Mean length14.084365
Min length5

Characters and Unicode

Total characters78464
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAcute Inpatient
2nd rowOther
3rd rowEmergency Department
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Outpatient 1452
26.1%
Other 1178
21.1%
Emergency Department 1166
20.9%
Skilled Nursing Facility 901
16.2%
Acute Inpatient 874
15.7%

Length

2023-05-08T21:59:00.972718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:01.077502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
outpatient 1452
15.4%
other 1178
12.5%
emergency 1166
12.4%
department 1166
12.4%
skilled 901
9.6%
nursing 901
9.6%
facility 901
9.6%
acute 874
9.3%
inpatient 874
9.3%

Most occurring characters

ValueCountFrequency (%)
t 11389
14.5%
e 9943
12.7%
n 6433
 
8.2%
i 5930
 
7.6%
r 4411
 
5.6%
a 4393
 
5.6%
3842
 
4.9%
p 3492
 
4.5%
u 3227
 
4.1%
c 2941
 
3.7%
Other values (16) 22463
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65209
83.1%
Uppercase Letter 9413
 
12.0%
Space Separator 3842
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 11389
17.5%
e 9943
15.2%
n 6433
9.9%
i 5930
9.1%
r 4411
 
6.8%
a 4393
 
6.7%
p 3492
 
5.4%
u 3227
 
4.9%
c 2941
 
4.5%
l 2703
 
4.1%
Other values (7) 10347
15.9%
Uppercase Letter
ValueCountFrequency (%)
O 2630
27.9%
E 1166
12.4%
D 1166
12.4%
S 901
 
9.6%
N 901
 
9.6%
F 901
 
9.6%
A 874
 
9.3%
I 874
 
9.3%
Space Separator
ValueCountFrequency (%)
3842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74622
95.1%
Common 3842
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 11389
15.3%
e 9943
13.3%
n 6433
 
8.6%
i 5930
 
7.9%
r 4411
 
5.9%
a 4393
 
5.9%
p 3492
 
4.7%
u 3227
 
4.3%
c 2941
 
3.9%
l 2703
 
3.6%
Other values (15) 19760
26.5%
Common
ValueCountFrequency (%)
3842
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 11389
14.5%
e 9943
12.7%
n 6433
 
8.2%
i 5930
 
7.6%
r 4411
 
5.6%
a 4393
 
5.6%
3842
 
4.9%
p 3492
 
4.5%
u 3227
 
4.1%
c 2941
 
3.7%
Other values (16) 22463
28.6%

CLAIM_STATUS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
Approved
3682 
Denied
1889 

Length

Max length8
Median length8
Mean length7.3218453
Min length6

Characters and Unicode

Total characters40790
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDenied
2nd rowDenied
3rd rowDenied
4th rowDenied
5th rowDenied

Common Values

ValueCountFrequency (%)
Approved 3682
66.1%
Denied 1889
33.9%

Length

2023-05-08T21:59:01.173218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:01.269417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
approved 3682
66.1%
denied 1889
33.9%

Most occurring characters

ValueCountFrequency (%)
e 7460
18.3%
p 7364
18.1%
d 5571
13.7%
A 3682
9.0%
r 3682
9.0%
o 3682
9.0%
v 3682
9.0%
D 1889
 
4.6%
n 1889
 
4.6%
i 1889
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35219
86.3%
Uppercase Letter 5571
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7460
21.2%
p 7364
20.9%
d 5571
15.8%
r 3682
10.5%
o 3682
10.5%
v 3682
10.5%
n 1889
 
5.4%
i 1889
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 3682
66.1%
D 1889
33.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 40790
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7460
18.3%
p 7364
18.1%
d 5571
13.7%
A 3682
9.0%
r 3682
9.0%
o 3682
9.0%
v 3682
9.0%
D 1889
 
4.6%
n 1889
 
4.6%
i 1889
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7460
18.3%
p 7364
18.1%
d 5571
13.7%
A 3682
9.0%
r 3682
9.0%
o 3682
9.0%
v 3682
9.0%
D 1889
 
4.6%
n 1889
 
4.6%
i 1889
 
4.6%

PAYERS
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
BCBS
1680 
Medicare
1394 
Aetna
951 
Cigna
718 
Humana
552 

Length

Max length8
Median length6
Mean length5.4491115
Min length3

Characters and Unicode

Total characters30357
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedicare
2nd rowMedicare
3rd rowHumana
4th rowMedicare
5th rowCigna

Common Values

ValueCountFrequency (%)
BCBS 1680
30.2%
Medicare 1394
25.0%
Aetna 951
17.1%
Cigna 718
12.9%
Humana 552
 
9.9%
UHG 276
 
5.0%

Length

2023-05-08T21:59:01.345939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:01.446378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
bcbs 1680
30.2%
medicare 1394
25.0%
aetna 951
17.1%
cigna 718
12.9%
humana 552
 
9.9%
uhg 276
 
5.0%

Most occurring characters

ValueCountFrequency (%)
a 4167
13.7%
e 3739
12.3%
B 3360
11.1%
C 2398
 
7.9%
n 2221
 
7.3%
i 2112
 
7.0%
S 1680
 
5.5%
r 1394
 
4.6%
c 1394
 
4.6%
d 1394
 
4.6%
Other values (9) 6498
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19194
63.2%
Uppercase Letter 11163
36.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4167
21.7%
e 3739
19.5%
n 2221
11.6%
i 2112
11.0%
r 1394
 
7.3%
c 1394
 
7.3%
d 1394
 
7.3%
t 951
 
5.0%
g 718
 
3.7%
u 552
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 3360
30.1%
C 2398
21.5%
S 1680
15.0%
M 1394
12.5%
A 951
 
8.5%
H 828
 
7.4%
U 276
 
2.5%
G 276
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 30357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4167
13.7%
e 3739
12.3%
B 3360
11.1%
C 2398
 
7.9%
n 2221
 
7.3%
i 2112
 
7.0%
S 1680
 
5.5%
r 1394
 
4.6%
c 1394
 
4.6%
d 1394
 
4.6%
Other values (9) 6498
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4167
13.7%
e 3739
12.3%
B 3360
11.1%
C 2398
 
7.9%
n 2221
 
7.3%
i 2112
 
7.0%
S 1680
 
5.5%
r 1394
 
4.6%
c 1394
 
4.6%
d 1394
 
4.6%
Other values (9) 6498
21.4%

DENIAL_CATEGORY
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
NOT_APPLICABLE
3682 
Prior Authorization
1176 
Eligibility
414 
Coding Error
 
207
Benefit Exhausted
 
63

Length

Max length19
Median length14
Mean length14.750494
Min length6

Characters and Unicode

Total characters82175
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEligibility
2nd rowCoding Error
3rd rowPrior Authorization
4th rowBenefit Exhausted
5th rowCoding Error

Common Values

ValueCountFrequency (%)
NOT_APPLICABLE 3682
66.1%
Prior Authorization 1176
 
21.1%
Eligibility 414
 
7.4%
Coding Error 207
 
3.7%
Benefit Exhausted 63
 
1.1%
Others 29
 
0.5%

Length

2023-05-08T21:59:01.543087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:01.648381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
not_applicable 3682
52.5%
prior 1176
 
16.8%
authorization 1176
 
16.8%
eligibility 414
 
5.9%
coding 207
 
2.9%
error 207
 
2.9%
benefit 63
 
0.9%
exhausted 63
 
0.9%
others 29
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 8540
 
10.4%
P 8540
 
10.4%
L 7364
 
9.0%
i 5454
 
6.6%
E 4366
 
5.3%
r 4178
 
5.1%
o 3942
 
4.8%
C 3889
 
4.7%
B 3745
 
4.6%
O 3711
 
4.5%
Other values (20) 28446
34.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 51201
62.3%
Lowercase Letter 25846
31.5%
Connector Punctuation 3682
 
4.5%
Space Separator 1446
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5454
21.1%
r 4178
16.2%
o 3942
15.3%
t 2921
11.3%
n 1446
 
5.6%
h 1268
 
4.9%
a 1239
 
4.8%
u 1239
 
4.8%
z 1176
 
4.6%
l 828
 
3.2%
Other values (8) 2155
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
A 8540
16.7%
P 8540
16.7%
L 7364
14.4%
E 4366
8.5%
C 3889
7.6%
B 3745
7.3%
O 3711
7.2%
N 3682
7.2%
I 3682
7.2%
T 3682
7.2%
Connector Punctuation
ValueCountFrequency (%)
_ 3682
100.0%
Space Separator
ValueCountFrequency (%)
1446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77047
93.8%
Common 5128
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 8540
 
11.1%
P 8540
 
11.1%
L 7364
 
9.6%
i 5454
 
7.1%
E 4366
 
5.7%
r 4178
 
5.4%
o 3942
 
5.1%
C 3889
 
5.0%
B 3745
 
4.9%
O 3711
 
4.8%
Other values (18) 23318
30.3%
Common
ValueCountFrequency (%)
_ 3682
71.8%
1446
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 8540
 
10.4%
P 8540
 
10.4%
L 7364
 
9.0%
i 5454
 
6.6%
E 4366
 
5.3%
r 4178
 
5.1%
o 3942
 
4.8%
C 3889
 
4.7%
B 3745
 
4.6%
O 3711
 
4.5%
Other values (20) 28446
34.6%

PROCEDURE_CODE
Categorical

Distinct135
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
03CL0ZZ
 
200
0T768DZ
 
185
5A1945Z
 
179
0D9670Z
 
178
03CK0ZZ
 
171
Other values (130)
4658 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters38997
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.4%

Sample

1st row0RGA071
2nd row0W3P8ZZ
3rd row0D9670Z
4th row4A023N7
5th row06BQ4ZZ

Common Values

ValueCountFrequency (%)
03CL0ZZ 200
 
3.6%
0T768DZ 185
 
3.3%
5A1945Z 179
 
3.2%
0D9670Z 178
 
3.2%
03CK0ZZ 171
 
3.1%
027034Z 143
 
2.6%
B548ZZA 135
 
2.4%
8E0W4CZ 135
 
2.4%
0SPC08Z 130
 
2.3%
0SB20ZZ 126
 
2.3%
Other values (125) 3989
71.6%

Length

2023-05-08T21:59:01.732948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03cl0zz 200
 
3.6%
0t768dz 185
 
3.3%
5a1945z 179
 
3.2%
0d9670z 178
 
3.2%
03ck0zz 171
 
3.1%
027034z 143
 
2.6%
b548zza 135
 
2.4%
8e0w4cz 135
 
2.4%
0spc08z 130
 
2.3%
0sb20zz 126
 
2.3%
Other values (125) 3989
71.6%

Most occurring characters

ValueCountFrequency (%)
Z 6989
17.9%
0 6504
16.7%
3 2972
 
7.6%
4 2068
 
5.3%
B 1882
 
4.8%
2 1804
 
4.6%
1 1664
 
4.3%
8 1629
 
4.2%
5 1457
 
3.7%
A 1424
 
3.7%
Other values (24) 10604
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20930
53.7%
Uppercase Letter 18067
46.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 6989
38.7%
B 1882
 
10.4%
A 1424
 
7.9%
D 1089
 
6.0%
C 860
 
4.8%
S 687
 
3.8%
X 574
 
3.2%
E 512
 
2.8%
P 477
 
2.6%
T 457
 
2.5%
Other values (14) 3116
17.2%
Decimal Number
ValueCountFrequency (%)
0 6504
31.1%
3 2972
14.2%
4 2068
 
9.9%
2 1804
 
8.6%
1 1664
 
8.0%
8 1629
 
7.8%
5 1457
 
7.0%
7 1262
 
6.0%
9 820
 
3.9%
6 750
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20930
53.7%
Latin 18067
46.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 6989
38.7%
B 1882
 
10.4%
A 1424
 
7.9%
D 1089
 
6.0%
C 860
 
4.8%
S 687
 
3.8%
X 574
 
3.2%
E 512
 
2.8%
P 477
 
2.6%
T 457
 
2.5%
Other values (14) 3116
17.2%
Common
ValueCountFrequency (%)
0 6504
31.1%
3 2972
14.2%
4 2068
 
9.9%
2 1804
 
8.6%
1 1664
 
8.0%
8 1629
 
7.8%
5 1457
 
7.0%
7 1262
 
6.0%
9 820
 
3.9%
6 750
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 6989
17.9%
0 6504
16.7%
3 2972
 
7.6%
4 2068
 
5.3%
B 1882
 
4.8%
2 1804
 
4.6%
1 1664
 
4.3%
8 1629
 
4.2%
5 1457
 
3.7%
A 1424
 
3.7%
Other values (24) 10604
27.2%
Distinct122
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
Precertification/authorization/notification/pre-treatment absent.
 
320
Plan procedures not followed.
 
314
Patient refused the service/procedure.
 
311
Dilation of Right Ureter with Intraluminal Device, Endo
 
159
Extirpation of Matter from L Int Carotid, Open Approach
 
152
Other values (117)
4315 

Length

Max length116
Median length68
Mean length51.65105
Min length27

Characters and Unicode

Total characters287748
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowCharges do not meet qualifications for emergent/urgent care.
2nd rowProcessed in Excess of charges.
3rd rowLifetime benefit maximum has been reached for this service/benefit category.
4th rowESRD network support adjustment.
5th rowThe procedure code is inconsistent with the provider type/specialty (taxonomy).

Common Values

ValueCountFrequency (%)
Precertification/authorization/notification/pre-treatment absent. 320
 
5.7%
Plan procedures not followed. 314
 
5.6%
Patient refused the service/procedure. 311
 
5.6%
Dilation of Right Ureter with Intraluminal Device, Endo 159
 
2.9%
Extirpation of Matter from L Int Carotid, Open Approach 152
 
2.7%
Respiratory Ventilation, 24-96 Consecutive Hours 125
 
2.2%
Dilation of 1 Cor Art with Drug-elut Intra, Perc Approach 123
 
2.2%
Drainage of Stomach with Drainage Device, Via Opening 117
 
2.1%
Extirpation of Matter from R Int Carotid, Open Approach 108
 
1.9%
Measure Cardiac Sampl & Pressure, Bilateral, Perc 96
 
1.7%
Other values (112) 3746
67.2%

Length

2023-05-08T21:59:01.836405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 2995
 
7.6%
approach 1603
 
4.1%
the 1139
 
2.9%
with 1061
 
2.7%
open 948
 
2.4%
perc 883
 
2.2%
endo 836
 
2.1%
excision 513
 
1.3%
not 487
 
1.2%
dilation 407
 
1.0%
Other values (327) 28594
72.5%

Most occurring characters

ValueCountFrequency (%)
33984
 
11.8%
e 25696
 
8.9%
o 20938
 
7.3%
r 20155
 
7.0%
t 19985
 
6.9%
i 18332
 
6.4%
n 17708
 
6.2%
a 16792
 
5.8%
s 11018
 
3.8%
c 10791
 
3.8%
Other values (53) 92349
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 218593
76.0%
Space Separator 33984
 
11.8%
Uppercase Letter 25059
 
8.7%
Other Punctuation 8074
 
2.8%
Decimal Number 1044
 
0.4%
Dash Punctuation 859
 
0.3%
Open Punctuation 60
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25696
11.8%
o 20938
9.6%
r 20155
9.2%
t 19985
9.1%
i 18332
 
8.4%
n 17708
 
8.1%
a 16792
 
7.7%
s 11018
 
5.0%
c 10791
 
4.9%
p 9544
 
4.4%
Other values (16) 47634
21.8%
Uppercase Letter
ValueCountFrequency (%)
P 3073
12.3%
A 2773
11.1%
C 2226
 
8.9%
E 2128
 
8.5%
D 1783
 
7.1%
O 1520
 
6.1%
R 1520
 
6.1%
I 1433
 
5.7%
V 1266
 
5.1%
T 1119
 
4.5%
Other values (12) 6218
24.8%
Other Punctuation
ValueCountFrequency (%)
, 4217
52.2%
. 1802
22.3%
/ 1707
21.1%
' 208
 
2.6%
& 140
 
1.7%
Decimal Number
ValueCountFrequency (%)
6 301
28.8%
9 238
22.8%
2 221
21.2%
4 161
15.4%
1 123
11.8%
Space Separator
ValueCountFrequency (%)
33984
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 859
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 243652
84.7%
Common 44096
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25696
 
10.5%
o 20938
 
8.6%
r 20155
 
8.3%
t 19985
 
8.2%
i 18332
 
7.5%
n 17708
 
7.3%
a 16792
 
6.9%
s 11018
 
4.5%
c 10791
 
4.4%
p 9544
 
3.9%
Other values (38) 72693
29.8%
Common
ValueCountFrequency (%)
33984
77.1%
, 4217
 
9.6%
. 1802
 
4.1%
/ 1707
 
3.9%
- 859
 
1.9%
6 301
 
0.7%
9 238
 
0.5%
2 221
 
0.5%
' 208
 
0.5%
4 161
 
0.4%
Other values (5) 398
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33984
 
11.8%
e 25696
 
8.9%
o 20938
 
7.3%
r 20155
 
7.0%
t 19985
 
6.9%
i 18332
 
6.4%
n 17708
 
6.2%
a 16792
 
5.8%
s 11018
 
3.8%
c 10791
 
3.8%
Other values (53) 92349
32.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
23
2750 
22
904 
99
766 
11
669 
21
482 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters11142
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row23
3rd row23
4th row99
5th row99

Common Values

ValueCountFrequency (%)
23 2750
49.4%
22 904
 
16.2%
99 766
 
13.7%
11 669
 
12.0%
21 482
 
8.7%

Length

2023-05-08T21:59:01.927735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:02.019389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
23 2750
49.4%
22 904
 
16.2%
99 766
 
13.7%
11 669
 
12.0%
21 482
 
8.7%

Most occurring characters

ValueCountFrequency (%)
2 5040
45.2%
3 2750
24.7%
1 1820
 
16.3%
9 1532
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11142
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5040
45.2%
3 2750
24.7%
1 1820
 
16.3%
9 1532
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5040
45.2%
3 2750
24.7%
1 1820
 
16.3%
9 1532
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5040
45.2%
3 2750
24.7%
1 1820
 
16.3%
9 1532
 
13.7%

REVENUE_CENTER_CODE
Real number (ℝ)

Distinct100
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.32382
Minimum23
Maximum983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:59:02.120355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile258
Q1300
median307
Q3460
95-th percentile730
Maximum983
Range960
Interquartile range (IQR)160

Descriptive statistics

Standard deviation162.27526
Coefficient of variation (CV)0.40035954
Kurtosis1.6162923
Mean405.32382
Median Absolute Deviation (MAD)36
Skewness1.4961746
Sum2258059
Variance26333.259
MonotonicityNot monotonic
2023-05-08T21:59:02.228913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 990
17.8%
301 810
14.5%
636 354
 
6.4%
305 265
 
4.8%
420 251
 
4.5%
510 234
 
4.2%
450 232
 
4.2%
250 198
 
3.6%
730 153
 
2.7%
320 116
 
2.1%
Other values (90) 1968
35.3%
ValueCountFrequency (%)
23 3
 
0.1%
200 8
 
0.1%
214 1
 
< 0.1%
250 198
3.6%
251 10
 
0.2%
255 5
 
0.1%
258 57
 
1.0%
259 3
 
0.1%
260 64
 
1.1%
270 54
 
1.0%
ValueCountFrequency (%)
983 1
 
< 0.1%
982 1
 
< 0.1%
981 6
 
0.1%
975 1
 
< 0.1%
972 3
 
0.1%
964 1
 
< 0.1%
960 1
 
< 0.1%
943 58
1.0%
942 2
 
< 0.1%
940 15
 
0.3%
Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4281099
Minimum0
Maximum70
Zeros20
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:59:02.337278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.4463531
Coefficient of variation (CV)2.6548853
Kurtosis46.729085
Mean2.4281099
Median Absolute Deviation (MAD)0
Skewness6.4264785
Sum13527
Variance41.555468
MonotonicityNot monotonic
2023-05-08T21:59:02.414214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 4838
86.8%
6 289
 
5.2%
30 133
 
2.4%
3 113
 
2.0%
2 64
 
1.1%
4 46
 
0.8%
62 28
 
0.5%
7 26
 
0.5%
0 20
 
0.4%
9 8
 
0.1%
Other values (3) 6
 
0.1%
ValueCountFrequency (%)
0 20
 
0.4%
1 4838
86.8%
2 64
 
1.1%
3 113
 
2.0%
4 46
 
0.8%
6 289
 
5.2%
7 26
 
0.5%
9 8
 
0.1%
30 133
 
2.4%
50 1
 
< 0.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
62 28
 
0.5%
51 4
 
0.1%
50 1
 
< 0.1%
30 133
2.4%
9 8
 
0.1%
7 26
 
0.5%
6 289
5.2%
4 46
 
0.8%
3 113
 
2.0%

MEDICARE_STATUS
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
10
4327 
20
1075 
0
 
169

Length

Max length2
Median length2
Mean length1.9696643
Min length1

Characters and Unicode

Total characters10973
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row10
3rd row10
4th row10
5th row20

Common Values

ValueCountFrequency (%)
10 4327
77.7%
20 1075
 
19.3%
0 169
 
3.0%

Length

2023-05-08T21:59:02.505443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:02.764511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
10 4327
77.7%
20 1075
 
19.3%
0 169
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 5571
50.8%
1 4327
39.4%
2 1075
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5571
50.8%
1 4327
39.4%
2 1075
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 10973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5571
50.8%
1 4327
39.4%
2 1075
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5571
50.8%
1 4327
39.4%
2 1075
 
9.8%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
2
1980 
4
1805 
1
1786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5571
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row4
5th row2

Common Values

ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

Length

2023-05-08T21:59:02.840475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:02.928132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

Most occurring characters

ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5571
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5571
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1980
35.5%
4 1805
32.4%
1 1786
32.1%

ADMIT_TYPE_CODE
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
1
2127 
3
1735 
2
1709 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5571
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

Length

2023-05-08T21:59:03.002496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T21:59:03.089515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

Most occurring characters

ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5571
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5571
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2127
38.2%
3 1735
31.1%
2 1709
30.7%

MS_DRG
Real number (ℝ)

Distinct33
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.53294
Minimum42
Maximum920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2023-05-08T21:59:03.174251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile149
Q1247
median309
Q3442
95-th percentile872
Maximum920
Range878
Interquartile range (IQR)195

Descriptive statistics

Standard deviation206.44995
Coefficient of variation (CV)0.55417906
Kurtosis0.84743287
Mean372.53294
Median Absolute Deviation (MAD)104
Skewness1.1810481
Sum2075381
Variance42621.584
MonotonicityNot monotonic
2023-05-08T21:59:03.264609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
442 375
 
6.7%
205 271
 
4.9%
292 267
 
4.8%
176 243
 
4.4%
233 227
 
4.1%
309 226
 
4.1%
202 216
 
3.9%
291 210
 
3.8%
683 206
 
3.7%
149 206
 
3.7%
Other values (23) 3124
56.1%
ValueCountFrequency (%)
42 120
2.2%
69 105
 
1.9%
149 206
3.7%
176 243
4.4%
202 216
3.9%
205 271
4.9%
233 227
4.1%
247 110
2.0%
264 37
 
0.7%
280 140
2.5%
ValueCountFrequency (%)
920 147
 
2.6%
872 138
 
2.5%
871 157
2.8%
690 73
 
1.3%
683 206
3.7%
641 135
 
2.4%
603 85
 
1.5%
481 112
 
2.0%
470 157
2.8%
442 375
6.7%

Interactions

2023-05-08T21:58:57.164330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:51.519629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.462824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.222770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.976140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.717563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.642835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.353034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.253342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:51.661667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.559836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.326961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.075581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.813927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.737123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.464077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.340603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:51.798960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.657454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.419944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.172529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.904006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.828294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.559180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.424207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:51.884686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.751700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.501536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.256640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.998841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.916871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.649129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.512698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:51.977780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.843102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.596653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.345576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.258501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.999794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.739661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.599947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.082473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.933307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.696264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.435235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.354024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.089842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.840578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.686756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.278287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.029368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.789103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.521747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.453181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.175975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.970600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.782332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:52.378811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.129924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:53.890230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:54.620021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:55.555552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:56.270390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T21:58:57.073049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-08T21:59:03.367988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
APPROVED_CHARGEREASON_CODEAgeSERVICE_UNIT_QUANTITYTOTAL_CHARGESREVENUE_CENTER_CODEDISCHARGE_DISPOSITION_CODEMS_DRGBILL_TYPE_CODEGENDERRACESTATEPRESENT_ON_ADMITDUAL_STATUSENCOUNTER_TYPECLAIM_STATUSPAYERSDENIAL_CATEGORYPLACE_OF_SERVICE_CODEMEDICARE_STATUSADMIT_SOURCE_CODEADMIT_TYPE_CODE
APPROVED_CHARGE1.000-0.3010.0560.4160.9350.1420.3740.0480.3150.1650.1340.2790.1450.1340.1230.2110.0000.0920.1540.1110.1240.124
REASON_CODE-0.3011.000-0.009-0.158-0.0850.104-0.127-0.0170.0810.0000.0000.0550.0460.1410.0170.8940.0000.5020.0260.0120.0130.032
Age0.056-0.0091.0000.0350.0710.0130.099-0.0570.1580.1710.1380.3690.1610.1850.1740.1070.0000.1200.1670.7030.1820.189
SERVICE_UNIT_QUANTITY0.416-0.1580.0351.0000.4010.0240.2260.0440.0380.0000.0500.0000.0440.0000.0140.0040.0000.0000.0280.0170.0180.000
TOTAL_CHARGES0.935-0.0850.0710.4011.0000.1450.3650.0590.3170.1670.1350.2810.1450.1360.1230.2080.0000.0910.1510.1130.1290.128
REVENUE_CENTER_CODE0.1420.1040.0130.0240.1451.0000.0300.0320.2460.0630.0520.1230.0560.0670.0580.2340.0200.1130.0590.0660.0510.086
DISCHARGE_DISPOSITION_CODE0.374-0.1270.0990.2260.3650.0301.0000.1320.4240.1550.1740.2330.0510.0960.0700.0500.0340.0170.0360.0380.0930.086
MS_DRG0.048-0.017-0.0570.0440.0590.0320.1321.0000.1850.1890.1260.3880.1810.1640.1590.0630.0120.0310.1530.1550.1430.168
BILL_TYPE_CODE0.3150.0810.1580.0380.3170.2460.4240.1851.0000.1460.2480.2700.1840.1900.2220.1880.0290.0790.2060.1390.1850.174
GENDER0.1650.0000.1710.0000.1670.0630.1550.1890.1461.0000.1450.4060.0730.2180.2150.0000.0170.0000.1580.0840.0920.178
RACE0.1340.0000.1380.0500.1350.0520.1740.1260.2480.1451.0000.3310.1170.1500.1500.0000.0260.0000.1340.1660.0740.169
STATE0.2790.0550.3690.0000.2810.1230.2330.3880.2700.4060.3311.0000.4140.3820.3610.1460.0710.0650.3680.3600.4400.399
PRESENT_ON_ADMIT0.1450.0460.1610.0440.1450.0560.0510.1810.1840.0730.1170.4141.0000.2180.1530.0720.0000.0490.1290.1130.1440.107
DUAL_STATUS0.1340.1410.1850.0000.1360.0670.0960.1640.1900.2180.1500.3820.2181.0000.2020.1270.0170.1210.1990.1880.1810.200
ENCOUNTER_TYPE0.1230.0170.1740.0140.1230.0580.0700.1590.2220.2150.1500.3610.1530.2021.0000.0660.0180.0330.7420.1140.1310.159
CLAIM_STATUS0.2110.8940.1070.0040.2080.2340.0500.0630.1880.0000.0000.1460.0720.1270.0661.0000.0001.0000.0750.0230.0000.031
PAYERS0.0000.0000.0000.0000.0000.0200.0340.0120.0290.0170.0260.0710.0000.0170.0180.0001.0000.0000.0090.0000.0000.015
DENIAL_CATEGORY0.0920.5020.1200.0000.0910.1130.0170.0310.0790.0000.0000.0650.0490.1210.0331.0000.0001.0000.0440.0120.0000.035
PLACE_OF_SERVICE_CODE0.1540.0260.1670.0280.1510.0590.0360.1530.2060.1580.1340.3680.1290.1990.7420.0750.0090.0441.0000.1030.1610.581
MEDICARE_STATUS0.1110.0120.7030.0170.1130.0660.0380.1550.1390.0840.1660.3600.1130.1880.1140.0230.0000.0120.1031.0000.0740.022
ADMIT_SOURCE_CODE0.1240.0130.1820.0180.1290.0510.0930.1430.1850.0920.0740.4400.1440.1810.1310.0000.0000.0000.1610.0741.0000.080
ADMIT_TYPE_CODE0.1240.0320.1890.0000.1280.0860.0860.1680.1740.1780.1690.3990.1070.2000.1590.0310.0150.0350.5810.0220.0801.000

Missing values

2023-05-08T21:58:57.955230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-08T21:58:58.301469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

APPROVED_CHARGEREASON_CODEAgeSERVICE_UNIT_QUANTITYTOTAL_CHARGESBILL_TYPE_CODEHCPCS_CODEGENDERRACESTATECOUNTYCODEPRESENT_ON_ADMITDUAL_STATUSENCOUNTER_TYPECLAIM_STATUSPAYERSDENIAL_CATEGORYPROCEDURE_CODEPROCEDURE_DESCRIPTIONPLACE_OF_SERVICE_CODEREVENUE_CENTER_CODEDISCHARGE_DISPOSITION_CODEMEDICARE_STATUSADMIT_SOURCE_CODEADMIT_TYPE_CODEMS_DRG
044.6222.0381225.6213182306malewhiteNorth CarolinaCherokeeG9340Y5Acute InpatientDeniedMedicareEligibility0RGA071Charges do not meet qualifications for emergent/urgent care.2130012043683
11457.0627.084101638.06131J1644femalewhiteUtahSummitE114200OtherDeniedMedicareCoding Error0W3P8ZZProcessed in Excess of charges.2363611021470
2254.9574.0811326.9513176642femalewhiteDelawareNew CastleI48004Emergency DepartmentDeniedHumanaPrior Authorization0D9670ZLifetime benefit maximum has been reached for this service/benefit category.2340211041641
34.0932.092176.0913185610malewhiteConnecticutFairfieldI1002OtherDeniedMedicareBenefit Exhausted4A023N7ESRD network support adjustment.9930011043378
456.118.0691173.11131G0463malewhiteIowaPottawattamieI25507OtherDeniedCignaCoding Error06BQ4ZZThe procedure code is inconsistent with the provider type/specialty (taxonomy).9951012023312
556.8614.0921386.8613180053malewhiteConnecticutFairfieldM199102OtherDeniedHumanaEligibility4A023N7This care may be covered by another payer per coordination of benefits.9930111043378
6157.4218.0921250.4213171275malewhiteConnecticutFairfieldD12502OtherDeniedCignaEligibility4A023N7Expenses incurred prior to coverage.9935211043378
733.76111.069197.7613194760malewhiteIowaPottawattamieZ8601007OtherDeniedCignaEligibility06BQ4ZZPatient has not met the required eligibility requirements.9946012023312
8115.7322.0801232.7313182306malewhiteCaliforniaMaderaI48006Skilled Nursing FacilityDeniedAetnaEligibility0D9670ZCharges do not meet qualifications for emergent/urgent care.1130111042683
932.48116.0831125.4813136415femalewhiteTexasDallasM069Y0OutpatientDeniedHumanaEligibility0SB20ZZRevenue code and Procedure code do not match.2230011042149
APPROVED_CHARGEREASON_CODEAgeSERVICE_UNIT_QUANTITYTOTAL_CHARGESBILL_TYPE_CODEHCPCS_CODEGENDERRACESTATECOUNTYCODEPRESENT_ON_ADMITDUAL_STATUSENCOUNTER_TYPECLAIM_STATUSPAYERSDENIAL_CATEGORYPROCEDURE_CODEPROCEDURE_DESCRIPTIONPLACE_OF_SERVICE_CODEREVENUE_CENTER_CODEDISCHARGE_DISPOSITION_CODEMEDICARE_STATUSADMIT_SOURCE_CODEADMIT_TYPE_CODEMS_DRG
559010279.811.078110279.81111Q0511femalewhitePennsylvaniaDelawareZ123106OutpatientApprovedMedicareNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2325061021418
559111829.411.078611829.41111A4611femalewhitePennsylvaniaDelawareZ0181006OutpatientApprovedCignaNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2327061021418
559210714.011.078410714.01111A4206femalewhitePennsylvaniaDelawareI1006OutpatientApprovedMedicareNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2327261021418
55935230.111.07885230.11111P2028femalewhitePennsylvaniaDelawareE03906OutpatientApprovedBCBSNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2330061021418
559411224.211.078411224.2111189250femalewhitePennsylvaniaDelawareR97206OutpatientApprovedBCBSNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2330161021418
559511224.211.078611224.2111185018femalewhitePennsylvaniaDelawareR4206OutpatientApprovedMedicareNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2330561021418
559611604.911.078211604.9111187040femalewhitePennsylvaniaDelawareS92901A06OutpatientApprovedMedicareNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2330661021418
559711224.211.078111224.2111181005femalewhitePennsylvaniaDelawareF1720006OutpatientApprovedMedicareNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2330761021418
559811439.311.078711439.3111199100femalewhitePennsylvaniaDelawareE78506OutpatientApprovedHumanaNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2337061021418
559910714.011.078610714.0111197530femalewhitePennsylvaniaDelawareE78506OutpatientApprovedCignaNOT_APPLICABLE0SRC0J9Replace of R Knee Jt with Synth Sub, Cement, Open Approach2342061021418